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The performance profile: A multi–criteria performance evaluation method for test–based problems Cover

The performance profile: A multi–criteria performance evaluation method for test–based problems

Open Access
|Mar 2016

References

  1. Ashlock, D. and Lee, C. (2013). Agent-case embeddings for the analysis of evolved systems, IEEE Transactions on Evolutionary Computation17(2): 227–240.10.1109/TEVC.2012.2234464
  2. Axelrod, R. (1984). The Evolution of Cooperation, Basic Books, New York, NY.
  3. Beyer, H.-G. and Schwefel, H.-P. (2002). Evolution strategies—a comprehensive introduction, Natural Computing1(1): 3–52.10.1023/A:1015059928466
  4. Bucci, A., Pollack, J.B. and de Jong, E. (2004). Automated extraction of problem structure, in K. Deb et al. (Eds.), Genetic and Evolutionary Computation—GECCO-2004, Part I, Lecture Notes in Computer Science, Vol. 3102, Springer-Verlag, Berlin/Heidelberg, pp. 501–512.10.1007/978-3-540-24854-5_53
  5. Chong, S.Y., Tiño, P., Ku, D.C. and Xin, Y. (2012). Improving generalization performance in co-evolutionary learning, IEEE Transactions on Evolutionary Computation16(1): 70–85.10.1109/TEVC.2010.2051673
  6. Chong, S.Y., Tiño, P. and Yao, X. (2008). Measuring generalization performance in coevolutionary learning, IEEE Transactions on Evolutionary Computation12(4): 479–505.10.1109/TEVC.2007.907593
  7. Chong, S.Y., Tiño, P. and Yao, X. (2009). Relationship between generalization and diversity in coevolutionary learning, IEEE Transactions on Computational Intelligence and AI in Games1(3): 214–232.10.1109/TCIAIG.2009.2034269
  8. Chong, S.Y. and Yao, X. (2005). Behavioral diversity, choices and noise in the iterated prisoner’s dilemma, IEEE Transactions on Evolutionary Computation9(6): 540–551.10.1109/TEVC.2005.856200
  9. Darwen, P.J. and Yao, X. (2001). Why more choices cause less cooperation in iterated prisoner’s dilemma, Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, South Korea, Vol. 2, pp. 987–994.
  10. Darwen, P. and Yao, X. (2000). Does extra genetic diversity maintain escalation in a co-evolutionary arms race, International Journal of Knowledge-Based Intelligent Engineering Systems4(3): 191–200.
  11. de Jong, E.D. (2004). The incremental Pareto-coevolution archive, in K. Deb et al. (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference, Lecture Notes in Computer Science, Vol. 3102, Springer-Verlag, Berlin/Heidelberg, pp. 525–536.10.1007/978-3-540-24854-5_55
  12. Ficici, S.G. (2004). Solution Concepts in Coevolutionary Algorithms, Ph.D. thesis, Brandeis University, Waltham, MA.
  13. Fogel, D.B. (1991). The evolution of intelligent decision making in gaming, Cybernetics and Systems22(2): 223–236.10.1080/01969729108902281
  14. Fogel, D.B. (2001). Blondie24: Playing at the Edge of AI, Morgan Kaufmann Publishers, San Francisco, CA.10.1016/B978-155860783-5/50016-7
  15. Frean, M. (1996). The evolution of degrees of cooperation, Journal of Theoretical Biology182(4): 549–59.10.1006/jtbi.1996.0194
  16. Hart, S. and Mas-Colell, A. (2000). A simple adaptive procedure leading to correlated equilibrium, Econometrica68(5): 1127–1150.10.1111/1468-0262.00153
  17. Hillis, W.D. (1990). Co-evolving parasites improve simulated evolution as an optimization procedure, Physica D42(1–3): 228–234.10.1016/0167-2789(90)90076-2
  18. Jaśkowski, W. (2011). Algorithms for Test-Based Problems, Ph.D. thesis, Poznań University of Technology, Poznań.
  19. Jaśkowski, W. (2014). Systematic n-tuple networks for Othello position evaluation, ICGA Journal37(2): 85–96.10.3233/ICG-2014-37203
  20. Jaśkowski, W. and Krawiec, K. (2011). How many dimensions in cooptimization?, in N. Krasnogor (Ed.), Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, ACM, New York, NY, pp. 829–830.
  21. Jaśkowski, W., Krawiec, K. and Wieloch, B. (2008). Evolving strategy for a probabilistic game of imperfect information using genetic programming, Genetic Programming and Evolvable Machines9(4): 281–294.10.1007/s10710-008-9062-1
  22. Jaśkowski, W., Liskowski, P., Szubert, M. and Krawiec, K. (2013). Improving coevolution by random sampling, in C. Blum (Ed.), GECCO’13: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, ACM, Amsterdam, pp. 1141–1148.10.1145/2463372.2463512
  23. Jaśkowski, W., Szubert, M. and Liskowski, P. (2014). Multi-criteria comparison of coevolution and temporal difference learning on Othello, in A.I. Esparcia-Alcazar and A.M. Mora (Eds.), EvoApplications 2014, Lecture Notes in Computer Science, Vol. 8602, Springer, Berlin/Heidelberg, pp. 301–312.10.1007/978-3-662-45523-4_25
  24. Juillé, H. and Pollack, J.B. (1998). Coevolving the ideal trainer: Application to the discovery of cellular automata rules, Proceedings of the 3rd Annual Conference on Genetic Programming, Madison, WI, USA, pp. 519–527.
  25. Knowles, J.D., Watson, R.A. and Corne, D. (2001). Reducing local optima in single-objective problems by multi-objectivization, EMO’01: Proceedings of the 1st International Conference on Evolutionary Multi-Criterion Optimization, Zurich, Switzerland, pp. 269–283.
  26. Krawiec, K., Jaśkowski, W. and Szubert, M. (2011). Evolving small-board go players using coevolutionary temporal difference learning with archive, International Journal of Applied Mathematics and Computer Science21(4): 717–731, DOI: 10.2478/v10006-011-0057-3.10.2478/v10006-011-0057-3
  27. Lucas, S.M. (2007). Learning to play Othello with n-tuple systems, Australian Journal of Intelligent Information Processing Systems9(4): 1–20.
  28. Lucas, S.M. and Runarsson, T.P. (2006). Temporal difference learning versus co-evolution for acquiring Othello position evaluation, IEEE Symposium on Computational Intelligence and Games, Reno, NV, USA, pp. 52–59.
  29. Luke, S. and Wiegand, R.P. (2002). When coevolutionary algorithms exhibit evolutionary dynamics, in A.M. Barry (Ed.), GECCO 2002: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference, AAAI, New York, NY, pp. 236–241.
  30. Manning, E.P. (2010). Using resource-limited Nash memory to improve an Othello evaluation function, IEEE Transactions on Computational Intelligence and AI in Games2(1): 40–53.10.1109/TCIAIG.2010.2042598
  31. Nolfi, S. and Floreano, D. (1998). Coevolving predator and prey robots: Do “Arms races” arise in artificial evolution?, Artificial Life4(4): 311–335.10.1162/10645469856862010352236
  32. Pollack, J.B. and Blair, A.D. (1998). Co-evolution in the successful learning of backgammon strategy, Machine Learning32(3): 225–240.10.1023/A:1007417214905
  33. Popovici, E., Bucci, A., Wiegand, R.P. and de Jong, E.D. (2011). Coevolutionary principles, in G. Rozenberg et al. (Eds.), Handbook of Natural Computing, Springer-Verlag, Berlin/Heidelberg, pp. 987–1033.
  34. Popovici, E. and De Jong, K. (2009). Monotonicity versus performance in co-optimization, FOGA’09: Proceedings of the 10th ACM SIGEVO Workshop on Foundations of Genetic Algorithms, Orlando, FL, USA, pp. 151–170.
  35. Poundstone, W. (1992). Prisoner’s Dilemma: John von Neuman, Game Theory, and the Puzzle of the Bomb, Doubleday, NY.10.1063/1.2809809
  36. Reynolds, C. (1994). Competition, coevolution and the game of tag, in R.A. Brooks and P. Maes (Eds.), Artificial Life IV: Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems, MIT Press, Cambridge, MA, pp. 59–69.
  37. Runarsson, T. and Lucas, S. (2014). Preference learning for move prediction and evaluation function approximation in Othello, IEEE Transactions on Computational Intelligence and AI in Games6(3): 300–313.10.1109/TCIAIG.2014.2307272
  38. Samothrakis, S., Lucas, S., Runarsson, T. and Robles, D. (2012). Coevolving game-playing agents: Measuring performance and intransitivities, IEEE Transactions on Evolutionary Computation17(2): 1–15.10.1109/TEVC.2012.2208755
  39. Szubert, M., Jaśkowski, W. and Krawiec, K. (2009). Coevolutionary temporal difference learning for Othello, IEEE Symposium on Computational Intelligence and Games, Milan, Italy, pp. 104–111.
  40. Szubert, M., Jaśkowski, W. and Krawiec, K. (2011). Learning board evaluation function for Othello by hybridizing coevolution with temporal difference learning, Control and Cybernetics40(3): 805–831.
  41. Szubert, M., Jaśkowski, W. and Krawiec, K. (2013a). On scalability, generalization, and hybridization of coevolutionary learning: A case study for Othello, IEEE Transactions on Computational Intelligence and AI in Games5(3): 214–226.10.1109/TCIAIG.2013.2258919
  42. Szubert, M., Liskowski, P., Jaśkowski, W. and Krawiec, K. (2013b). Shaping fitness function for evolutionary learning of game strategies, in C. Blum (Ed.), GECCO’13: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, ACM, Amsterdam, pp. 1149–1156.10.1145/2463372.2463513
  43. Szubert, M., Jaśkowski, W., Liskowski, P. and Krawiec, K. (2015). The role of behavioral diversity and difficulty of opponents in coevolving game-playing agents, in M.A. Mora and G. Squilero (Eds.), EvoApplications 2015, Lecture Notes in Computer Science, Vol. 9028, Springer, pp. 394–405.10.1007/978-3-319-16549-3_32
  44. Watson, R.A. and Pollack, J.B. (2001). Coevolutionary dynamics in a minimal substrate, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), San Francisco, CA, USA, pp. 702–709.
  45. Yoshioka, T., Ishii, S. and Ito, M. (1998). Strategy acquisition for the game ”Othello” based on reinforcement learning, in S. Usui and T. Omori (Eds.), Proceedings of the Fifth International Conference on Neural Information Processing, ICONIP98, IOA Press, Kitakyushu, pp. 841–844.
DOI: https://doi.org/10.1515/amcs-2016-0015 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 215 - 229
Submitted on: Feb 13, 2015
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Published on: Mar 31, 2016
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2016 Wojciech Jaśkowski, Paweł Liskowski, Marcin Szubert, Krzysztof Krawiec, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.